shallow_IFN_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0c510ed3-c680-478c-875d-8eec61cd2335",
   "metadata": {},
   "source": [
    "## Shallow mutagenesis of NS5 and effect on IFN signaling\n",
    "Matt and Blake have identified 66 residues on NS5 that contact with its host cell target STAT2, a component of the JAK-STAT IFN signaling cascade. They want to integrate their data, which quantifies loss of IFN signaling per single aa mutant, with the DMS datasets generated by NS5 DMS analysis.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "02c39881-bbb2-4acd-81c4-ffb0ae5befea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using dms_tools2 2.6.10\n"
     ]
    }
   ],
   "source": [
    "# import necessary Python modules and packages\n",
    "import glob\n",
    "import os\n",
    "import subprocess\n",
    "import shutil\n",
    "\n",
    "import Bio.SeqIO\n",
    "\n",
    "import dms_tools2\n",
    "from dms_tools2.ipython_utils import showPDF\n",
    "from dms_tools2.plot import COLOR_BLIND_PALETTE_GRAY as CBPALETTE\n",
    "import dms_tools2.prefs\n",
    "import dms_tools2.utils\n",
    "print(f\"Using dms_tools2 {dms_tools2.__version__}\")\n",
    "\n",
    "from IPython.display import display, HTML\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "import altair as alt\n",
    "from plotnine import *\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "import dms_variants.plotnine_themes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1696111a-c8ea-4f85-be5c-fa64638cc49b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# specify out directory \n",
    "outdir = \"./results/shallow_mut_scanning\"\n",
    "os.makedirs(outdir, exist_ok=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d5285107-8bbf-4f31-9ce1-897fcb5222cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# specify datadir\n",
    "datadir = \"./data/\"\n",
    "\n",
    "#specify resultsdir\n",
    "resultsdir = './results/'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc667fff-4449-4246-b4d3-59dd91613455",
   "metadata": {},
   "source": [
    "Using the dms-view input file as a starting point, we have most of the values we hope to compare to IFN signaling, except for site entropy and site neffective. We can calculate those now:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c95cd28d-2fff-4d0b-9ba4-caba8f0bace9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mut_temp</th>\n",
       "      <th>prefs</th>\n",
       "      <th>entropy</th>\n",
       "      <th>neffective</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0A</td>\n",
       "      <td>0.01184</td>\n",
       "      <td>1.896747</td>\n",
       "      <td>6.664182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0C</td>\n",
       "      <td>0.00554</td>\n",
       "      <td>1.896747</td>\n",
       "      <td>6.664182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0D</td>\n",
       "      <td>0.02762</td>\n",
       "      <td>1.896747</td>\n",
       "      <td>6.664182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0E</td>\n",
       "      <td>0.02146</td>\n",
       "      <td>1.896747</td>\n",
       "      <td>6.664182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0F</td>\n",
       "      <td>0.01317</td>\n",
       "      <td>1.896747</td>\n",
       "      <td>6.664182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19000</th>\n",
       "      <td>904T</td>\n",
       "      <td>0.00472</td>\n",
       "      <td>1.021642</td>\n",
       "      <td>2.777751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19001</th>\n",
       "      <td>904V</td>\n",
       "      <td>0.00797</td>\n",
       "      <td>1.021642</td>\n",
       "      <td>2.777751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19002</th>\n",
       "      <td>904W</td>\n",
       "      <td>0.02101</td>\n",
       "      <td>1.021642</td>\n",
       "      <td>2.777751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19003</th>\n",
       "      <td>904Y</td>\n",
       "      <td>0.00746</td>\n",
       "      <td>1.021642</td>\n",
       "      <td>2.777751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19004</th>\n",
       "      <td>904*</td>\n",
       "      <td>0.81193</td>\n",
       "      <td>1.021642</td>\n",
       "      <td>2.777751</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>19005 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      mut_temp    prefs   entropy  neffective\n",
       "0           0A  0.01184  1.896747    6.664182\n",
       "1           0C  0.00554  1.896747    6.664182\n",
       "2           0D  0.02762  1.896747    6.664182\n",
       "3           0E  0.02146  1.896747    6.664182\n",
       "4           0F  0.01317  1.896747    6.664182\n",
       "...        ...      ...       ...         ...\n",
       "19000     904T  0.00472  1.021642    2.777751\n",
       "19001     904V  0.00797  1.021642    2.777751\n",
       "19002     904W  0.02101  1.021642    2.777751\n",
       "19003     904Y  0.00746  1.021642    2.777751\n",
       "19004     904*  0.81193  1.021642    2.777751\n",
       "\n",
       "[19005 rows x 4 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a concatenated dataframe for all tiles of 'prefs_Huh-7.5.csv'\n",
    "d = {}\n",
    "tile_list = ['tile_1', 'tile_2', 'tile_3', 'tile_4', \n",
    "             'tile_5', 'tile_6', 'tile_7', 'tile_8']\n",
    "for tile in tile_list:\n",
    "    tilepath = os.path.join(resultsdir + tile + \"/prefs_with_stop/prefs_Huh-7.5.csv\")\n",
    "    d[tile] = pd.read_csv(tilepath)\n",
    "alltiles_prefs = pd.concat([d['tile_1'], d['tile_2'], d['tile_3'], d['tile_4'], \n",
    "                                d['tile_5'], d['tile_6'], d['tile_7'], d['tile_8']])\n",
    "\n",
    "charlist = alltiles_prefs.columns[1:].tolist()\n",
    "\n",
    "# now we can use this merged prefs file as input for dms_tools2.prefs.prefsEntropy\n",
    "alltiles_prefs_Entropy = (alltiles_prefs\n",
    "                  .melt(id_vars='site',\n",
    "                        var_name='mutation',\n",
    "                        value_name = 'prefs')\n",
    "                  .merge(dms_tools2.prefs.prefsEntropy(alltiles_prefs, charlist)\n",
    "                         [['site', 'entropy', 'neffective']],\n",
    "                         on='site', validate='many_to_one')\n",
    "                  .assign(mut_temp=lambda x: x['site'].astype(str) + x['mutation'])\n",
    "                  [['mut_temp','prefs','entropy', 'neffective']]\n",
    "                 )\n",
    "alltiles_prefs_Entropy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "af1fdbeb-d6f7-4eb8-9355-ffe30102c1ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>site</th>\n",
       "      <th>wt_aa</th>\n",
       "      <th>mut_aa</th>\n",
       "      <th>IFNsignal_rep1</th>\n",
       "      <th>IFNsignal_rep2</th>\n",
       "      <th>IFNsignal_rep3</th>\n",
       "      <th>IFNsignal_rep4</th>\n",
       "      <th>IFNsignal_rep5</th>\n",
       "      <th>IFNsignal_rep6</th>\n",
       "    </tr>\n",
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       "      <th>0</th>\n",
       "      <td>45</td>\n",
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       "      <td>A</td>\n",
       "      <td>0.757896</td>\n",
       "      <td>0.645745</td>\n",
       "      <td>0.691731</td>\n",
       "      <td>0.798957</td>\n",
       "      <td>0.893993</td>\n",
       "      <td>0.921810</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>45</td>\n",
       "      <td>K</td>\n",
       "      <td>C</td>\n",
       "      <td>0.685382</td>\n",
       "      <td>0.709307</td>\n",
       "      <td>0.705830</td>\n",
       "      <td>0.825626</td>\n",
       "      <td>0.871360</td>\n",
       "      <td>0.871070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>45</td>\n",
       "      <td>K</td>\n",
       "      <td>D</td>\n",
       "      <td>0.723599</td>\n",
       "      <td>0.748491</td>\n",
       "      <td>0.797137</td>\n",
       "      <td>0.894440</td>\n",
       "      <td>1.149193</td>\n",
       "      <td>0.981557</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>45</td>\n",
       "      <td>K</td>\n",
       "      <td>E</td>\n",
       "      <td>1.732656</td>\n",
       "      <td>1.778684</td>\n",
       "      <td>1.822542</td>\n",
       "      <td>2.223671</td>\n",
       "      <td>1.972298</td>\n",
       "      <td>2.011419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>45</td>\n",
       "      <td>K</td>\n",
       "      <td>F</td>\n",
       "      <td>2.036651</td>\n",
       "      <td>2.104278</td>\n",
       "      <td>2.035019</td>\n",
       "      <td>0.928235</td>\n",
       "      <td>1.047443</td>\n",
       "      <td>1.243766</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>855</td>\n",
       "      <td>H</td>\n",
       "      <td>T</td>\n",
       "      <td>0.867426</td>\n",
       "      <td>0.867245</td>\n",
       "      <td>0.925385</td>\n",
       "      <td>0.821238</td>\n",
       "      <td>1.052951</td>\n",
       "      <td>0.860901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>855</td>\n",
       "      <td>H</td>\n",
       "      <td>V</td>\n",
       "      <td>0.752261</td>\n",
       "      <td>0.713486</td>\n",
       "      <td>0.631645</td>\n",
       "      <td>0.777266</td>\n",
       "      <td>0.861843</td>\n",
       "      <td>0.736862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>855</td>\n",
       "      <td>H</td>\n",
       "      <td>W</td>\n",
       "      <td>0.659746</td>\n",
       "      <td>0.711049</td>\n",
       "      <td>0.667034</td>\n",
       "      <td>0.872954</td>\n",
       "      <td>0.805128</td>\n",
       "      <td>0.885427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>855</td>\n",
       "      <td>H</td>\n",
       "      <td>Y</td>\n",
       "      <td>0.742681</td>\n",
       "      <td>0.786027</td>\n",
       "      <td>0.713447</td>\n",
       "      <td>0.921469</td>\n",
       "      <td>0.946302</td>\n",
       "      <td>0.939008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>855</td>\n",
       "      <td>H</td>\n",
       "      <td>stop</td>\n",
       "      <td>155.945059</td>\n",
       "      <td>142.604383</td>\n",
       "      <td>148.994048</td>\n",
       "      <td>187.482583</td>\n",
       "      <td>143.972143</td>\n",
       "      <td>176.223123</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>189 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     site wt_aa mut_aa  IFNsignal_rep1  IFNsignal_rep2  IFNsignal_rep3  \\\n",
       "0      45     K      A        0.757896        0.645745        0.691731   \n",
       "1      45     K      C        0.685382        0.709307        0.705830   \n",
       "2      45     K      D        0.723599        0.748491        0.797137   \n",
       "3      45     K      E        1.732656        1.778684        1.822542   \n",
       "4      45     K      F        2.036651        2.104278        2.035019   \n",
       "..    ...   ...    ...             ...             ...             ...   \n",
       "184   855     H      T        0.867426        0.867245        0.925385   \n",
       "185   855     H      V        0.752261        0.713486        0.631645   \n",
       "186   855     H      W        0.659746        0.711049        0.667034   \n",
       "187   855     H      Y        0.742681        0.786027        0.713447   \n",
       "188   855     H   stop      155.945059      142.604383      148.994048   \n",
       "\n",
       "     IFNsignal_rep4  IFNsignal_rep5  IFNsignal_rep6  \n",
       "0          0.798957        0.893993        0.921810  \n",
       "1          0.825626        0.871360        0.871070  \n",
       "2          0.894440        1.149193        0.981557  \n",
       "3          2.223671        1.972298        2.011419  \n",
       "4          0.928235        1.047443        1.243766  \n",
       "..              ...             ...             ...  \n",
       "184        0.821238        1.052951        0.860901  \n",
       "185        0.777266        0.861843        0.736862  \n",
       "186        0.872954        0.805128        0.885427  \n",
       "187        0.921469        0.946302        0.939008  \n",
       "188      187.482583      143.972143      176.223123  \n",
       "\n",
       "[189 rows x 9 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# specify and load IFN signaling data\n",
    "IFN_signaling_file = os.path.join(datadir + \"NS5_IFN_signaling.csv\")\n",
    "IFN_signaling = pd.read_csv(IFN_signaling_file)\n",
    "IFN_signaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d412b007-2360-4546-bb1f-34937ddc7a62",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mutation</th>\n",
       "      <th>mean_IFN</th>\n",
       "      <th>median_IFN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>K45A</td>\n",
       "      <td>0.790447</td>\n",
       "      <td>0.798957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>K45C</td>\n",
       "      <td>0.796639</td>\n",
       "      <td>0.825626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>K45D</td>\n",
       "      <td>0.914163</td>\n",
       "      <td>0.894440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>K45E</td>\n",
       "      <td>1.961723</td>\n",
       "      <td>1.972298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>K45F</td>\n",
       "      <td>1.471748</td>\n",
       "      <td>1.243766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>H855T</td>\n",
       "      <td>0.905544</td>\n",
       "      <td>0.867245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>H855V</td>\n",
       "      <td>0.744221</td>\n",
       "      <td>0.736862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>H855W</td>\n",
       "      <td>0.788319</td>\n",
       "      <td>0.805128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>H855Y</td>\n",
       "      <td>0.861251</td>\n",
       "      <td>0.921469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>H855*</td>\n",
       "      <td>159.855256</td>\n",
       "      <td>148.994048</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>189 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    mutation    mean_IFN  median_IFN\n",
       "0       K45A    0.790447    0.798957\n",
       "1       K45C    0.796639    0.825626\n",
       "2       K45D    0.914163    0.894440\n",
       "3       K45E    1.961723    1.972298\n",
       "4       K45F    1.471748    1.243766\n",
       "..       ...         ...         ...\n",
       "184    H855T    0.905544    0.867245\n",
       "185    H855V    0.744221    0.736862\n",
       "186    H855W    0.788319    0.805128\n",
       "187    H855Y    0.861251    0.921469\n",
       "188    H855*  159.855256  148.994048\n",
       "\n",
       "[189 rows x 3 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# replace \"stop\" with \"*\"\n",
    "IFN_signaling = (IFN_signaling\n",
    "                 .replace(to_replace = \"stop\", value = \"*\")\n",
    "                 .assign(mutation=lambda x: x['wt_aa'] + x['site'].astype(str) + x['mut_aa'])\n",
    "                )\n",
    "\n",
    "# add columns with mean and median IFN signaling \n",
    "IFN_signaling['mean_IFN'] = IFN_signaling.iloc[:, 4:9].mean(axis=1)\n",
    "IFN_signaling['median_IFN'] = IFN_signaling.iloc[:, 4:9].median(axis=1)\n",
    "\n",
    "# make a neat version of this dataframe without rep values\n",
    "IFN_signaling_neat = IFN_signaling[['mutation', 'mean_IFN', 'median_IFN']]\n",
    "IFN_signaling_neat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "41373e5d-8ad1-476e-a53b-40664f9cfb47",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mutation</th>\n",
       "      <th>mean_IFN</th>\n",
       "      <th>median_IFN</th>\n",
       "      <th>site</th>\n",
       "      <th>wildtype</th>\n",
       "      <th>mutant</th>\n",
       "      <th>prefs</th>\n",
       "      <th>entropy</th>\n",
       "      <th>neffective</th>\n",
       "      <th>muteffect_C636</th>\n",
       "      <th>muteffect_Huh75</th>\n",
       "      <th>foldchange_C636</th>\n",
       "      <th>foldchange_Huh75</th>\n",
       "      <th>diffsel_Huh75_vs_C636</th>\n",
       "      <th>mutation_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>K45R</td>\n",
       "      <td>0.790649</td>\n",
       "      <td>0.802734</td>\n",
       "      <td>45</td>\n",
       "      <td>K</td>\n",
       "      <td>R</td>\n",
       "      <td>0.03879</td>\n",
       "      <td>2.885072</td>\n",
       "      <td>17.904862</td>\n",
       "      <td>-0.7691</td>\n",
       "      <td>-1.019</td>\n",
       "      <td>0.5868</td>\n",
       "      <td>0.4935</td>\n",
       "      <td>-0.022313</td>\n",
       "      <td>all-others</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   mutation  mean_IFN  median_IFN  site wildtype mutant    prefs   entropy  \\\n",
       "14     K45R  0.790649    0.802734    45        K      R  0.03879  2.885072   \n",
       "\n",
       "    neffective  muteffect_C636  muteffect_Huh75  foldchange_C636  \\\n",
       "14   17.904862         -0.7691           -1.019           0.5868   \n",
       "\n",
       "    foldchange_Huh75  diffsel_Huh75_vs_C636 mutation_type  \n",
       "14            0.4935              -0.022313    all-others  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# now merge all DMS data and IFN \"shallow\" mutational scan data\n",
    "NS5_DMS_IFNshallow = IFN_signaling_neat.merge(NS5_DMS_allsites, on='mutation')\n",
    "NS5_DMS_IFNshallow.query('mutation == \"K45R\"')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09435bde-dda0-4738-b981-e58078d899c6",
   "metadata": {},
   "source": [
    "Now we can produce Altair interactive visualization plots and color code based on site entropy (or site neffective -  I am showing both here but can only color code on one method per plot)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "45b58ded-e805-48c2-a138-c17964e34d09",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving interactive plot to ./results/shallow_mut_scanning/mean_IFN_signaling.html\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<div id=\"altair-viz-1e18d0a3eaa249c49b9695e10b8ba856\"></div>\n",
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       "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
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       "      outputDiv = document.getElementById(\"altair-viz-1e18d0a3eaa249c49b9695e10b8ba856\");\n",
       "    }\n",
       "    const paths = {\n",
       "      \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
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       "        new Promise(function(resolve, reject) {\n",
       "          var s = document.createElement('script');\n",
       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "          s.async = true;\n",
       "          s.onload = () => {\n",
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       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
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       "\n",
       "    function displayChart(vegaEmbed) {\n",
       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
       "    }\n",
       "\n",
       "    if(typeof define === \"function\" && define.amd) {\n",
       "      requirejs.config({paths});\n",
       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
       "    } else {\n",
       "      maybeLoadScript(\"vega\", \"5\")\n",
       "        .then(() => maybeLoadScript(\"vega-lite\", \"4.17.0\"))\n",
       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
       "        .catch(showError)\n",
       "        .then(() => displayChart(vegaEmbed));\n",
       "    }\n",
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\"mutation_type\": \"all-others\", \"dummy\": 0}, {\"mutation\": \"K45V\", \"mean IFN signaling\": 0.8091992028, \"median_IFN\": 0.8547293229999999, \"site\": 45, \"wildtype\": \"K\", \"mutant\": \"V\", \"prefs\": 0.04394, \"site entropy\": 2.8850722992585487, \"site neffective\": 17.904862056976345, \"muteffect_C636\": -1.5353, \"muteffect\": -0.8392, \"foldchange_C636\": 0.345, \"foldchange_Huh75\": 0.5589999999999999, \"diffsel_Huh75_vs_C636\": 0.6929327316553642, \"mutation_type\": \"all-others\", \"dummy\": 0}, {\"mutation\": \"K45W\", \"mean IFN signaling\": 0.8887815293999999, \"median_IFN\": 0.83552198, \"site\": 45, \"wildtype\": \"K\", \"mutant\": \"W\", \"prefs\": 0.012620000000000001, \"site entropy\": 2.8850722992585487, \"site neffective\": 17.904862056976345, \"muteffect_C636\": -2.9165, \"muteffect\": -2.639, \"foldchange_C636\": 0.1324, \"foldchange_Huh75\": 0.1605, \"diffsel_Huh75_vs_C636\": 0.2946353598549737, \"mutation_type\": \"all-others\", \"dummy\": 0}, 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\"diffsel_Huh75_vs_C636\": 0.5115519451430751, \"mutation_type\": \"all-others\", \"dummy\": 0}, {\"mutation\": \"H855E\", \"mean IFN signaling\": 23.302332030000002, \"median_IFN\": 21.55854417, \"site\": 855, \"wildtype\": \"H\", \"mutant\": \"E\", \"prefs\": 0.00503, \"site entropy\": 1.6999179537996005, \"site neffective\": 5.4734982935661725, \"muteffect_C636\": -4.1666, \"muteffect\": -4.1436, \"foldchange_C636\": 0.0557, \"foldchange_Huh75\": 0.0566, \"diffsel_Huh75_vs_C636\": 0.0003712033864288541, \"mutation_type\": \"all-others\", \"dummy\": 0}, {\"mutation\": \"H855F\", \"mean IFN signaling\": 0.9672597639999999, \"median_IFN\": 0.989015144, \"site\": 855, \"wildtype\": \"H\", \"mutant\": \"F\", \"prefs\": 0.40169, \"site entropy\": 1.6999179537996005, \"site neffective\": 5.4734982935661725, \"muteffect_C636\": 0.6698, \"muteffect\": 2.1758, \"foldchange_C636\": 1.5909, \"foldchange_Huh75\": 4.5184, \"diffsel_Huh75_vs_C636\": 1.4062584300508294, \"mutation_type\": \"all-others\", \"dummy\": 0}, {\"mutation\": \"H855G\", \"mean IFN signaling\": 1.3412603828000003, \"median_IFN\": 1.263140992, \"site\": 855, \"wildtype\": \"H\", \"mutant\": \"G\", \"prefs\": 0.0024100000000000002, \"site entropy\": 1.6999179537996005, \"site neffective\": 5.4734982935661725, \"muteffect_C636\": -5.2332, \"muteffect\": -5.2051, \"foldchange_C636\": 0.0266, \"foldchange_Huh75\": 0.0271, \"diffsel_Huh75_vs_C636\": 0.0016568273141329645, \"mutation_type\": \"all-others\", \"dummy\": 0}, {\"mutation\": \"H855H\", \"mean IFN signaling\": 0.9852681914, \"median_IFN\": 0.993045112, \"site\": 855, \"wildtype\": \"H\", \"mutant\": \"H\", \"prefs\": 0.0889, \"site entropy\": 1.6999179537996005, \"site neffective\": 5.4734982935661725, \"muteffect_C636\": 0.0, \"muteffect\": 0.0, \"foldchange_C636\": 1.0, \"foldchange_Huh75\": 1.0, \"diffsel_Huh75_vs_C636\": null, \"mutation_type\": \"all-others\", \"dummy\": 0}, {\"mutation\": \"H855I\", \"mean IFN signaling\": 1.1429172058, \"median_IFN\": 1.084071614, \"site\": 855, \"wildtype\": \"H\", \"mutant\": \"I\", \"prefs\": 0.00297, \"site entropy\": 1.6999179537996005, \"site neffective\": 5.4734982935661725, \"muteffect_C636\": -4.8394, \"muteffect\": -4.9036, \"foldchange_C636\": 0.0349, \"foldchange_Huh75\": 0.0334, \"diffsel_Huh75_vs_C636\": 0.0003712033864288541, \"mutation_type\": \"all-others\", \"dummy\": 0}, {\"mutation\": \"H855K\", \"mean IFN signaling\": 56.418094155999995, \"median_IFN\": 43.34865607, \"site\": 855, \"wildtype\": \"H\", \"mutant\": \"K\", \"prefs\": 0.00518, \"site entropy\": 1.6999179537996005, \"site neffective\": 5.4734982935661725, \"muteffect_C636\": -4.0651, \"muteffect\": -4.1012, \"foldchange_C636\": 0.0597, \"foldchange_Huh75\": 0.0583, \"diffsel_Huh75_vs_C636\": 0.0016568273141329645, \"mutation_type\": \"all-others\", \"dummy\": 0}, {\"mutation\": \"H855L\", \"mean IFN signaling\": 0.84814496175, \"median_IFN\": 0.8359981075, \"site\": 855, \"wildtype\": \"H\", 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\"mutation_type\": \"all-others\", \"dummy\": 0}, {\"mutation\": \"H855*\", \"mean IFN signaling\": 159.85525562000004, \"median_IFN\": 148.99404750000002, \"site\": 855, \"wildtype\": \"H\", \"mutant\": \"*\", \"prefs\": 0.00534, \"site entropy\": 1.6999179537996005, \"site neffective\": 5.4734982935661725, \"muteffect_C636\": -4.0579, \"muteffect\": -4.0573, \"foldchange_C636\": 0.06, \"foldchange_Huh75\": 0.0601, \"diffsel_Huh75_vs_C636\": null, \"mutation_type\": \"stop\", \"dummy\": 0}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.HConcatChart(...)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# select point nearest mouse\n",
    "nearest = alt.selection(type='single', empty='none', nearest=True, on='mouseover')\n",
    "\n",
    "# create the basic chart\n",
    "basechart = (\n",
    " alt.Chart(NS5_DMS_IFNshallow\n",
    "           .rename(columns={'muteffect_Huh75': 'muteffect',\n",
    "                            'mean_IFN': 'mean IFN signaling',\n",
    "                            'entropy': 'site entropy',\n",
    "                            'neffective' : 'site neffective',\n",
    "                            })\n",
    "           .assign(dummy=0)\n",
    "           )\n",
    " .add_selection(nearest)\n",
    " .encode(fill=alt.condition(nearest, alt.value('blue'), alt.value('lightblue')),\n",
    "         opacity=alt.condition(nearest, alt.value(1), alt.value(0.8)),\n",
    "         tooltip=['mutation', 'muteffect', 'site entropy', 'site neffective'],\n",
    "         color='site entropy:Q'\n",
    "         )\n",
    " .interactive()\n",
    " )\n",
    "\n",
    "# side-by-side interactive plots to select mutations\n",
    "mean_chart = (\n",
    " basechart.encode(x='muteffect:Q',\n",
    "              y='mean IFN signaling:Q'\n",
    "              )\n",
    "      .mark_point()\n",
    "      .properties(width=500,\n",
    "                  height=500)\n",
    " |\n",
    " basechart.encode(x=alt.X('dummy:O', title=None),\n",
    "              y='mean IFN signaling:Q',           \n",
    "              )\n",
    "      .properties(width=50,\n",
    "                  height=500)\n",
    "      .mark_tick()\n",
    " )\n",
    "\n",
    "# save the interactive plot\n",
    "plotfile = os.path.join(outdir, 'mean_IFN_signaling.html')\n",
    "print(f\"Saving interactive plot to {plotfile}\")\n",
    "mean_chart.save(plotfile)\n",
    "\n",
    "# show the chart\n",
    "mean_chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "031039ea-f661-42e5-ae86-3a3a6a52620a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving interactive plot to ./results/shallow_mut_scanning/median_IFN_signaling.html\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<div id=\"altair-viz-4e9b73d3fcb4491bb9894404c5401977\"></div>\n",
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       "    let outputDiv = document.currentScript.previousElementSibling;\n",
       "    if (outputDiv.id !== \"altair-viz-4e9b73d3fcb4491bb9894404c5401977\") {\n",
       "      outputDiv = document.getElementById(\"altair-viz-4e9b73d3fcb4491bb9894404c5401977\");\n",
       "    }\n",
       "    const paths = {\n",
       "      \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.17.0?noext\",\n",
       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
       "    };\n",
       "\n",
       "    function maybeLoadScript(lib, version) {\n",
       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
       "      return (VEGA_DEBUG[key] == version) ?\n",
       "        Promise.resolve(paths[lib]) :\n",
       "        new Promise(function(resolve, reject) {\n",
       "          var s = document.createElement('script');\n",
       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "          s.async = true;\n",
       "          s.onload = () => {\n",
       "            VEGA_DEBUG[key] = version;\n",
       "            return resolve(paths[lib]);\n",
       "          };\n",
       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
       "          s.src = paths[lib];\n",
       "        });\n",
       "    }\n",
       "\n",
       "    function showError(err) {\n",
       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
       "      throw err;\n",
       "    }\n",
       "\n",
       "    function displayChart(vegaEmbed) {\n",
       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
       "    }\n",
       "\n",
       "    if(typeof define === \"function\" && define.amd) {\n",
       "      requirejs.config({paths});\n",
       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
       "    } else {\n",
       "      maybeLoadScript(\"vega\", \"5\")\n",
       "        .then(() => maybeLoadScript(\"vega-lite\", \"4.17.0\"))\n",
       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
       "        .catch(showError)\n",
       "        .then(() => displayChart(vegaEmbed));\n",
       "    }\n",
       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"hconcat\": [{\"mark\": \"point\", \"encoding\": {\"color\": {\"field\": \"site entropy\", \"type\": \"quantitative\"}, \"fill\": {\"condition\": {\"value\": \"blue\", \"selection\": \"selector003\"}, \"value\": \"lightblue\"}, \"opacity\": {\"condition\": {\"value\": 1, \"selection\": \"selector003\"}, \"value\": 0.8}, \"tooltip\": [{\"field\": \"mutation\", \"type\": \"nominal\"}, {\"field\": \"muteffect\", \"type\": \"quantitative\"}, {\"field\": \"site entropy\", \"type\": \"quantitative\"}, {\"field\": \"site neffective\", \"type\": \"quantitative\"}], \"x\": {\"field\": \"muteffect\", \"type\": \"quantitative\"}, \"y\": {\"field\": \"median IFN signaling\", \"type\": \"quantitative\"}}, \"height\": 500, \"selection\": {\"selector003\": {\"type\": \"single\", \"empty\": \"none\", \"nearest\": true, \"on\": \"mouseover\"}, \"selector004\": {\"type\": \"interval\", \"bind\": \"scales\", 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\"wildtype\": \"I\", \"mutant\": \"D\", \"prefs\": 0.01601, \"site entropy\": 1.9675183756826093, \"site neffective\": 7.152903625106914, \"muteffect_C636\": -4.963, \"muteffect\": -4.9932, \"foldchange_C636\": 0.0321, \"foldchange_Huh75\": 0.0314, \"diffsel_Huh75_vs_C636\": -1.8783276839870472e-05, \"mutation_type\": \"all-others\", \"dummy\": 0}, {\"mutation\": \"I853E\", \"mean_IFN\": 0.7445948, \"median IFN signaling\": 0.712938, \"site\": 853, \"wildtype\": \"I\", \"mutant\": \"E\", \"prefs\": 0.021840000000000002, \"site entropy\": 1.9675183756826093, \"site neffective\": 7.152903625106914, \"muteffect_C636\": -4.5227, \"muteffect\": -4.5452, \"foldchange_C636\": 0.0435, \"foldchange_Huh75\": 0.0428, \"diffsel_Huh75_vs_C636\": -1.8783276839870472e-05, \"mutation_type\": \"all-others\", \"dummy\": 0}, {\"mutation\": \"I853F\", \"mean_IFN\": 0.9840164, \"median IFN signaling\": 0.92437, \"site\": 853, \"wildtype\": \"I\", \"mutant\": \"F\", \"prefs\": 0.027339999999999996, \"site entropy\": 1.9675183756826093, \"site neffective\": 7.152903625106914, \"muteffect_C636\": -4.1805, \"muteffect\": -4.2212, \"foldchange_C636\": 0.0551, \"foldchange_Huh75\": 0.0536, \"diffsel_Huh75_vs_C636\": 0.0001895947212827943, \"mutation_type\": \"all-others\", \"dummy\": 0}, {\"mutation\": \"I853G\", \"mean_IFN\": 1.0192558, \"median IFN signaling\": 1.010993, \"site\": 853, \"wildtype\": \"I\", \"mutant\": \"G\", \"prefs\": 0.0086, \"site entropy\": 1.9675183756826093, \"site neffective\": 7.152903625106914, \"muteffect_C636\": -5.8517, \"muteffect\": -5.8898, \"foldchange_C636\": 0.0173, \"foldchange_Huh75\": 0.0169, \"diffsel_Huh75_vs_C636\": -1.8783276839870472e-05, \"mutation_type\": \"all-others\", \"dummy\": 0}, {\"mutation\": \"I853H\", \"mean_IFN\": 0.8147757999999999, \"median IFN signaling\": 0.7758010000000001, \"site\": 853, \"wildtype\": \"I\", \"mutant\": \"H\", \"prefs\": 0.03506, \"site entropy\": 1.9675183756826093, \"site neffective\": 7.152903625106914, \"muteffect_C636\": -3.7795, \"muteffect\": -3.8623, \"foldchange_C636\": 0.0728, \"foldchange_Huh75\": 0.0688, \"diffsel_Huh75_vs_C636\": -1.8783276839870472e-05, \"mutation_type\": \"all-others\", \"dummy\": 0}, {\"mutation\": \"I853I\", \"mean_IFN\": 1.0071728, \"median IFN signaling\": 1.013314, \"site\": 853, \"wildtype\": \"I\", \"mutant\": \"I\", \"prefs\": 0.5099100000000001, \"site entropy\": 1.9675183756826093, \"site neffective\": 7.152903625106914, \"muteffect_C636\": 0.0, \"muteffect\": 0.0, \"foldchange_C636\": 1.0, \"foldchange_Huh75\": 1.0, \"diffsel_Huh75_vs_C636\": null, \"mutation_type\": \"all-others\", \"dummy\": 0}, {\"mutation\": \"I853K\", \"mean_IFN\": 1.0531368, \"median IFN signaling\": 1.0502799999999999, \"site\": 853, \"wildtype\": \"I\", \"mutant\": \"K\", \"prefs\": 0.02546, \"site entropy\": 1.9675183756826093, \"site neffective\": 7.152903625106914, \"muteffect_C636\": -4.4557, \"muteffect\": -4.3239, \"foldchange_C636\": 0.0456, \"foldchange_Huh75\": 0.0499, \"diffsel_Huh75_vs_C636\": 0.2451087263209701, \"mutation_type\": \"all-others\", \"dummy\": 0}, {\"mutation\": \"I853L\", \"mean_IFN\": 0.9725809999999999, \"median IFN signaling\": 0.9785360000000001, \"site\": 853, \"wildtype\": \"I\", \"mutant\": \"L\", \"prefs\": 0.12922, \"site entropy\": 1.9675183756826093, \"site neffective\": 7.152903625106914, \"muteffect_C636\": -3.1476, \"muteffect\": -1.9804, \"foldchange_C636\": 0.1128, \"foldchange_Huh75\": 0.2534, \"diffsel_Huh75_vs_C636\": 0.4962151653046685, \"mutation_type\": \"all-others\", \"dummy\": 0}, {\"mutation\": \"I853M\", \"mean_IFN\": 1.3223989999999999, \"median IFN signaling\": 1.174415, \"site\": 853, \"wildtype\": \"I\", \"mutant\": \"M\", \"prefs\": 0.00325, \"site entropy\": 1.9675183756826093, \"site neffective\": 7.152903625106914, \"muteffect_C636\": -2.3665, \"muteffect\": -7.2937, \"foldchange_C636\": 0.1939, \"foldchange_Huh75\": 0.0064, \"diffsel_Huh75_vs_C636\": -0.1196977809120182, \"mutation_type\": \"all-others\", \"dummy\": 0}, {\"mutation\": \"I853N\", \"mean_IFN\": 0.9691164000000001, \"median IFN signaling\": 0.9849100000000001, \"site\": 853, \"wildtype\": \"I\", \"mutant\": \"N\", \"prefs\": 0.025509999999999998, \"site entropy\": 1.9675183756826093, \"site neffective\": 7.152903625106914, \"muteffect_C636\": -4.2922, \"muteffect\": -4.3211, \"foldchange_C636\": 0.051, \"foldchange_Huh75\": 0.05, \"diffsel_Huh75_vs_C636\": -1.8783276839870472e-05, \"mutation_type\": \"all-others\", \"dummy\": 0}, {\"mutation\": \"I853P\", \"mean_IFN\": 92.070368, \"median IFN signaling\": 84.26308, \"site\": 853, \"wildtype\": \"I\", \"mutant\": \"P\", \"prefs\": 0.008440000000000001, \"site entropy\": 1.9675183756826093, \"site neffective\": 7.152903625106914, \"muteffect_C636\": -5.8729, \"muteffect\": -5.9169, \"foldchange_C636\": 0.0171, \"foldchange_Huh75\": 0.0166, \"diffsel_Huh75_vs_C636\": 0.0001895947212827943, \"mutation_type\": \"all-others\", \"dummy\": 0}, {\"mutation\": \"I853Q\", \"mean_IFN\": 1.123945, \"median IFN signaling\": 1.127738, \"site\": 853, \"wildtype\": \"I\", \"mutant\": \"Q\", \"prefs\": 0.0278, \"site entropy\": 1.9675183756826093, \"site neffective\": 7.152903625106914, \"muteffect_C636\": -4.1746, \"muteffect\": -4.1971, \"foldchange_C636\": 0.0554, \"foldchange_Huh75\": 0.0545, \"diffsel_Huh75_vs_C636\": -1.8783276839870472e-05, \"mutation_type\": \"all-others\", \"dummy\": 0}, {\"mutation\": \"I853R\", \"mean_IFN\": 1.1950224, \"median IFN signaling\": 1.098563, \"site\": 853, \"wildtype\": \"I\", \"mutant\": \"R\", \"prefs\": 0.01709, \"site entropy\": 1.9675183756826093, \"site neffective\": 7.152903625106914, \"muteffect_C636\": -4.6034, \"muteffect\": -4.899, \"foldchange_C636\": 0.0411, \"foldchange_Huh75\": 0.0335, \"diffsel_Huh75_vs_C636\": 0.4975375883331706, \"mutation_type\": \"all-others\", \"dummy\": 0}, {\"mutation\": \"I853S\", \"mean_IFN\": 0.7381838000000001, \"median IFN signaling\": 0.728957, \"site\": 853, \"wildtype\": \"I\", \"mutant\": \"S\", \"prefs\": 0.00838, \"site entropy\": 1.9675183756826093, \"site neffective\": 7.152903625106914, \"muteffect_C636\": -5.9008, \"muteffect\": -5.9271, \"foldchange_C636\": 0.0167, \"foldchange_Huh75\": 0.0164, \"diffsel_Huh75_vs_C636\": -1.8783276839870472e-05, \"mutation_type\": \"all-others\", \"dummy\": 0}, {\"mutation\": \"I853T\", \"mean_IFN\": 0.8651820000000001, \"median IFN signaling\": 0.8282459999999999, \"site\": 853, \"wildtype\": \"I\", \"mutant\": \"T\", \"prefs\": 0.0061600000000000005, \"site entropy\": 1.9675183756826093, \"site neffective\": 7.152903625106914, \"muteffect_C636\": -3.0062, \"muteffect\": -6.3712, \"foldchange_C636\": 0.1245, \"foldchange_Huh75\": 0.0121, \"diffsel_Huh75_vs_C636\": 0.25757434426724096, \"mutation_type\": \"all-others\", \"dummy\": 0}, {\"mutation\": \"I853V\", \"mean_IFN\": 0.9900428, \"median IFN signaling\": 0.986036, \"site\": 853, \"wildtype\": \"I\", \"mutant\": \"V\", \"prefs\": 0.00413, \"site entropy\": 1.9675183756826093, \"site neffective\": 7.152903625106914, \"muteffect_C636\": -1.3791, \"muteffect\": -6.9479999999999995, \"foldchange_C636\": 0.3845, \"foldchange_Huh75\": 0.0081, \"diffsel_Huh75_vs_C636\": -0.4973079462493949, \"mutation_type\": \"all-others\", \"dummy\": 0}, {\"mutation\": \"I853W\", \"mean_IFN\": 0.8573604000000001, \"median IFN signaling\": 0.8694350000000001, \"site\": 853, \"wildtype\": \"I\", \"mutant\": \"W\", \"prefs\": 0.01923, \"site entropy\": 1.9675183756826093, \"site neffective\": 7.152903625106914, \"muteffect_C636\": -4.6881, \"muteffect\": -4.7288, \"foldchange_C636\": 0.0388, \"foldchange_Huh75\": 0.0377, \"diffsel_Huh75_vs_C636\": -1.8783276839870472e-05, \"mutation_type\": \"all-others\", \"dummy\": 0}, {\"mutation\": \"I853Y\", \"mean_IFN\": 0.9082444000000001, \"median IFN signaling\": 0.920823, \"site\": 853, \"wildtype\": \"I\", \"mutant\": \"Y\", \"prefs\": 0.02165, \"site entropy\": 1.9675183756826093, \"site neffective\": 7.152903625106914, \"muteffect_C636\": -3.6415, \"muteffect\": -4.5578, \"foldchange_C636\": 0.0801, \"foldchange_Huh75\": 0.0425, \"diffsel_Huh75_vs_C636\": 0.25264127971684464, \"mutation_type\": \"all-others\", \"dummy\": 0}, {\"mutation\": \"I853*\", \"mean_IFN\": 161.13294000000002, \"median IFN signaling\": 152.7602, \"site\": 853, \"wildtype\": \"I\", \"mutant\": \"*\", \"prefs\": 0.01987, \"site entropy\": 1.9675183756826093, \"site neffective\": 7.152903625106914, \"muteffect_C636\": -4.6365, \"muteffect\": -4.6816, \"foldchange_C636\": 0.0402, \"foldchange_Huh75\": 0.039, \"diffsel_Huh75_vs_C636\": null, \"mutation_type\": \"stop\", \"dummy\": 0}, {\"mutation\": \"H855A\", \"mean_IFN\": 0.7014878108, \"median IFN signaling\": 0.701183263, \"site\": 855, \"wildtype\": \"H\", \"mutant\": \"A\", \"prefs\": 0.00293, \"site entropy\": 1.6999179537996005, \"site neffective\": 5.4734982935661725, \"muteffect_C636\": -4.9306, \"muteffect\": -4.9232, \"foldchange_C636\": 0.0328, \"foldchange_Huh75\": 0.033, \"diffsel_Huh75_vs_C636\": -0.07432937992893468, \"mutation_type\": \"all-others\", \"dummy\": 0}, {\"mutation\": \"H855C\", \"mean_IFN\": 0.6616384557999999, \"median IFN signaling\": 0.6722327640000001, \"site\": 855, \"wildtype\": \"H\", \"mutant\": \"C\", \"prefs\": 0.0326, \"site entropy\": 1.6999179537996005, \"site neffective\": 5.4734982935661725, \"muteffect_C636\": -0.6908, \"muteffect\": -1.4473, \"foldchange_C636\": 0.6195, \"foldchange_Huh75\": 0.3667, \"diffsel_Huh75_vs_C636\": -0.3392350855959874, \"mutation_type\": \"all-others\", \"dummy\": 0}, {\"mutation\": \"H855D\", \"mean_IFN\": 6.23184776, \"median IFN signaling\": 4.342051692, \"site\": 855, \"wildtype\": \"H\", \"mutant\": \"D\", \"prefs\": 0.00686, \"site entropy\": 1.6999179537996005, \"site neffective\": 5.4734982935661725, \"muteffect_C636\": -4.7738, \"muteffect\": -3.6959, \"foldchange_C636\": 0.0366, \"foldchange_Huh75\": 0.0772, \"diffsel_Huh75_vs_C636\": 0.5115519451430751, \"mutation_type\": \"all-others\", \"dummy\": 0}, {\"mutation\": \"H855E\", \"mean_IFN\": 23.302332030000002, \"median IFN signaling\": 21.55854417, \"site\": 855, \"wildtype\": \"H\", \"mutant\": \"E\", \"prefs\": 0.00503, \"site entropy\": 1.6999179537996005, \"site neffective\": 5.4734982935661725, \"muteffect_C636\": -4.1666, \"muteffect\": -4.1436, \"foldchange_C636\": 0.0557, \"foldchange_Huh75\": 0.0566, \"diffsel_Huh75_vs_C636\": 0.0003712033864288541, \"mutation_type\": \"all-others\", \"dummy\": 0}, {\"mutation\": \"H855F\", \"mean_IFN\": 0.9672597639999999, \"median IFN signaling\": 0.989015144, \"site\": 855, \"wildtype\": \"H\", \"mutant\": \"F\", \"prefs\": 0.40169, \"site entropy\": 1.6999179537996005, \"site neffective\": 5.4734982935661725, \"muteffect_C636\": 0.6698, \"muteffect\": 2.1758, \"foldchange_C636\": 1.5909, \"foldchange_Huh75\": 4.5184, \"diffsel_Huh75_vs_C636\": 1.4062584300508294, \"mutation_type\": \"all-others\", \"dummy\": 0}, {\"mutation\": \"H855G\", \"mean_IFN\": 1.3412603828000003, \"median IFN signaling\": 1.263140992, \"site\": 855, \"wildtype\": \"H\", \"mutant\": \"G\", \"prefs\": 0.0024100000000000002, \"site entropy\": 1.6999179537996005, \"site neffective\": 5.4734982935661725, \"muteffect_C636\": -5.2332, \"muteffect\": -5.2051, \"foldchange_C636\": 0.0266, \"foldchange_Huh75\": 0.0271, \"diffsel_Huh75_vs_C636\": 0.0016568273141329645, \"mutation_type\": \"all-others\", \"dummy\": 0}, {\"mutation\": \"H855H\", \"mean_IFN\": 0.9852681914, \"median IFN signaling\": 0.993045112, \"site\": 855, \"wildtype\": \"H\", \"mutant\": \"H\", \"prefs\": 0.0889, \"site entropy\": 1.6999179537996005, \"site neffective\": 5.4734982935661725, \"muteffect_C636\": 0.0, \"muteffect\": 0.0, \"foldchange_C636\": 1.0, \"foldchange_Huh75\": 1.0, \"diffsel_Huh75_vs_C636\": null, \"mutation_type\": \"all-others\", \"dummy\": 0}, {\"mutation\": \"H855I\", \"mean_IFN\": 1.1429172058, \"median IFN signaling\": 1.084071614, \"site\": 855, \"wildtype\": \"H\", \"mutant\": \"I\", \"prefs\": 0.00297, \"site entropy\": 1.6999179537996005, \"site neffective\": 5.4734982935661725, \"muteffect_C636\": -4.8394, \"muteffect\": -4.9036, \"foldchange_C636\": 0.0349, \"foldchange_Huh75\": 0.0334, \"diffsel_Huh75_vs_C636\": 0.0003712033864288541, \"mutation_type\": \"all-others\", \"dummy\": 0}, {\"mutation\": \"H855K\", \"mean_IFN\": 56.418094155999995, \"median IFN signaling\": 43.34865607, \"site\": 855, \"wildtype\": \"H\", \"mutant\": \"K\", \"prefs\": 0.00518, \"site entropy\": 1.6999179537996005, \"site neffective\": 5.4734982935661725, \"muteffect_C636\": -4.0651, \"muteffect\": -4.1012, \"foldchange_C636\": 0.0597, \"foldchange_Huh75\": 0.0583, \"diffsel_Huh75_vs_C636\": 0.0016568273141329645, \"mutation_type\": \"all-others\", \"dummy\": 0}, {\"mutation\": \"H855L\", \"mean_IFN\": 0.84814496175, \"median IFN signaling\": 0.8359981075, \"site\": 855, \"wildtype\": \"H\", \"mutant\": \"L\", \"prefs\": 0.0011300000000000001, \"site entropy\": 1.6999179537996005, \"site neffective\": 5.4734982935661725, \"muteffect_C636\": -6.8302, \"muteffect\": -6.2978, \"foldchange_C636\": 0.0088, \"foldchange_Huh75\": 0.0127, \"diffsel_Huh75_vs_C636\": 0.6255235272896462, \"mutation_type\": \"all-others\", \"dummy\": 0}, {\"mutation\": \"H855M\", \"mean_IFN\": 0.7556528924, \"median IFN signaling\": 0.757511501, \"site\": 855, \"wildtype\": \"H\", \"mutant\": \"M\", \"prefs\": 0.00563, \"site entropy\": 1.6999179537996005, \"site neffective\": 5.4734982935661725, \"muteffect_C636\": -3.9225, \"muteffect\": -3.9810000000000003, \"foldchange_C636\": 0.0659, \"foldchange_Huh75\": 0.0633, \"diffsel_Huh75_vs_C636\": 0.0016568273141329645, \"mutation_type\": \"all-others\", \"dummy\": 0}, {\"mutation\": \"H855N\", \"mean_IFN\": 97.68173187, \"median IFN signaling\": 96.867924515, \"site\": 855, \"wildtype\": \"H\", \"mutant\": \"N\", \"prefs\": 0.02108, \"site entropy\": 1.6999179537996005, \"site 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0.0279, \"diffsel_Huh75_vs_C636\": 0.04083670325860839, \"mutation_type\": \"all-others\", \"dummy\": 0}, {\"mutation\": \"H855T\", \"mean_IFN\": 0.9055437372, \"median IFN signaling\": 0.8672448759999999, \"site\": 855, \"wildtype\": \"H\", \"mutant\": \"T\", \"prefs\": 0.0015199999999999999, \"site entropy\": 1.6999179537996005, \"site neffective\": 5.4734982935661725, \"muteffect_C636\": -5.8609, \"muteffect\": -5.87, \"foldchange_C636\": 0.0172, \"foldchange_Huh75\": 0.0171, \"diffsel_Huh75_vs_C636\": 0.0003712033864288541, \"mutation_type\": \"all-others\", \"dummy\": 0}, {\"mutation\": \"H855V\", \"mean_IFN\": 0.7442206224000001, \"median IFN signaling\": 0.736862461, \"site\": 855, \"wildtype\": \"H\", \"mutant\": \"V\", \"prefs\": 0.0035700000000000003, \"site entropy\": 1.6999179537996005, \"site neffective\": 5.4734982935661725, \"muteffect_C636\": -4.6244, \"muteffect\": -4.6382, \"foldchange_C636\": 0.0405, \"foldchange_Huh75\": 0.0402, \"diffsel_Huh75_vs_C636\": 0.0016568273141329645, \"mutation_type\": \"all-others\", \"dummy\": 0}, {\"mutation\": \"H855W\", \"mean_IFN\": 0.7883185958000001, \"median IFN signaling\": 0.805128453, \"site\": 855, \"wildtype\": \"H\", \"mutant\": \"W\", \"prefs\": 0.21591999999999997, \"site entropy\": 1.6999179537996005, \"site neffective\": 5.4734982935661725, \"muteffect_C636\": 0.1639, \"muteffect\": 1.2802, \"foldchange_C636\": 1.1203, \"foldchange_Huh75\": 2.4287, \"diffsel_Huh75_vs_C636\": 0.8655258053596113, \"mutation_type\": \"all-others\", \"dummy\": 0}, {\"mutation\": \"H855Y\", \"mean_IFN\": 0.8612506246, \"median IFN signaling\": 0.92146916, \"site\": 855, \"wildtype\": \"H\", \"mutant\": \"Y\", \"prefs\": 0.18905999999999998, \"site entropy\": 1.6999179537996005, \"site neffective\": 5.4734982935661725, \"muteffect_C636\": 0.9540000000000001, \"muteffect\": 1.0886, \"foldchange_C636\": 1.9373, \"foldchange_Huh75\": 2.1267, \"diffsel_Huh75_vs_C636\": 0.2546592060191627, \"mutation_type\": \"all-others\", \"dummy\": 0}, {\"mutation\": \"H855*\", \"mean_IFN\": 159.85525562000004, \"median IFN signaling\": 148.99404750000002, \"site\": 855, \"wildtype\": \"H\", \"mutant\": \"*\", \"prefs\": 0.00534, \"site entropy\": 1.6999179537996005, \"site neffective\": 5.4734982935661725, \"muteffect_C636\": -4.0579, \"muteffect\": -4.0573, \"foldchange_C636\": 0.06, \"foldchange_Huh75\": 0.0601, \"diffsel_Huh75_vs_C636\": null, \"mutation_type\": \"stop\", \"dummy\": 0}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.HConcatChart(...)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# select point nearest mouse\n",
    "nearest = alt.selection(type='single', empty='none', nearest=True, on='mouseover')\n",
    "\n",
    "# create the basic chart\n",
    "basechart = (\n",
    " alt.Chart(NS5_DMS_IFNshallow\n",
    "           .rename(columns={'muteffect_Huh75': 'muteffect',\n",
    "                            'median_IFN': 'median IFN signaling',\n",
    "                            'entropy': 'site entropy',\n",
    "                            'neffective' : 'site neffective',\n",
    "                            })\n",
    "           .assign(dummy=0)\n",
    "           )\n",
    " .add_selection(nearest)\n",
    " .encode(fill=alt.condition(nearest, alt.value('blue'), alt.value('lightblue')),\n",
    "         opacity=alt.condition(nearest, alt.value(1), alt.value(0.8)),\n",
    "         tooltip=['mutation', 'muteffect', 'site entropy', 'site neffective'],\n",
    "         color='site entropy:Q'\n",
    "         )\n",
    " .interactive()\n",
    " )\n",
    "\n",
    "# side-by-side interactive plots to select mutations\n",
    "median_chart = (\n",
    " basechart.encode(x='muteffect:Q',\n",
    "              y='median IFN signaling:Q'\n",
    "              )\n",
    "      .mark_point()\n",
    "      .properties(width=500,\n",
    "                  height=500)\n",
    " |\n",
    " basechart.encode(x=alt.X('dummy:O', title=None),\n",
    "              y='median IFN signaling:Q',           \n",
    "              )\n",
    "      .properties(width=50,\n",
    "                  height=500)\n",
    "      .mark_tick()\n",
    " )\n",
    "\n",
    "# save the interactive plot\n",
    "plotfile = os.path.join(outdir, 'median_IFN_signaling.html')\n",
    "print(f\"Saving interactive plot to {plotfile}\")\n",
    "median_chart.save(plotfile)\n",
    "\n",
    "# show the chart\n",
    "median_chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aa0e2554-6b66-4ac6-ace8-4971d7312ca5",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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